Papers by Raghava Mutharaju
Knowledge-Driven Cross-Document Relation Extraction (2024.findings-acl)
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| Challenge: | Existing approaches to extract relationships between entities are based on sentence-level tasks, but they do not consider domain knowledge, which are assumed to be known to the reader when documents are authored. |
| Approach: | They propose to embed domain knowledge of entities with input text for cross-document RE by embedding domain knowledge with the document. |
| Outcome: | The proposed framework offers interpretability by producing explanatory text for predicted relations between entities and improves performance over baseline methods. |
JobXMLC: EXtreme Multi-Label Classification of Job Skills with Graph Neural Networks (2023.findings-eacl)
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Nidhi Goyal, Jushaan Kalra, Charu Sharma, Raghava Mutharaju, Niharika Sachdeva, Ponnurangam Kumaraguru
| Challenge: | Existing approaches to predict missing skills are limited to contextual modelling and do not exploit inter-relational structures like job-job and job-skill relationships. |
| Approach: | They propose a skill prediction framework that exploits structural relationships to predict missing skills using job descriptions. |
| Outcome: | The proposed framework outperforms the state-of-the-art approaches by 6% in precision and 3% in recall on real-world recruitment datasets. |